*3.2. Load Power Signatures*

As initially pointed out in Reference [14], each appliance has a power signature that is not necessarily a steady-state power, thus Figure 3 presents different appliances' power behaviors, and the appliances can be classified using power signatures [7,10,14,16,22], as shown in Figure 4.

**Figure 4.** Different appliance power signatures. (**a**) Constant behavior; (**b**) Multiple power states; (**c**) All-time constant; (**d**) Approximately linear variation; (**e**) Various operational stages; and (**f**) Power behavior with hard detection.

From Figure 4, an appliance can have a power signature with:


over time. The noises from current and voltage sensors are also aggregated into this power signature category. The printer is an example of this type of load, which has some power steps that vary and switch very quickly.

Hence, considering the possibility of such different appliances' signatures, it would be important to have a preliminary filter before using any appliance recognition technique, so as to increase the disaggregation accuracy. Thus, the next section presents the proposed approach, which uses CPT power terms and NILM techniques to detect the power signatures, before using the appliance classification method by means of the KNN algorithm.

The power signature behavior was aggregated into the 35 appliances dataset, as can be seen in Table 2. This characteristic is not used as features into the pattern recognition algorithm [15], but it is used to filter and increase accuracy in load detection.


**Table 2.** Household appliance dataset.
